Transductive Rademacher complexities for learning over a graph

Posted in Science on August 22, 2008


Transductive Rademacher complexities for learning over a graph

Recent investigations indicate the use of a probabilistic ”learning” perspective of tasks defined on a single graph, as opposed to the traditional algorithmical ”computational” point of view. This note discusses the use of Rademacher complexities in this setting, and illustrates the use of Kruskal’s algorithm for transductive inference based on a nearest neighbor rule.

Author: Kristiaan Pelckmans, Ku Leuven

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Tags: Science, Lectures, Computer Science, Machine Learning, VideoLectures.Net, Graphical Models